The Climate in Climate Economics
45 Pages Posted: 14 Jul 2021 Last revised: 4 Oct 2021
Date Written: July 12, 2021
We develop a generic and transparent calibration strategy for climate models used in economics.
The key idea is to choose the free model parameters to match the output of large-scale Earth System Models, which are run on pre-defined future emissions scenarios and collected in the Coupled Model Intercomparison Project, Phase 5 (CMIP5). We propose to jointly use four different test cases that are considered pivotal in the climate science literature. Two of these tests are highly idealized to allow for the separate quantitative examination of the carbon cycle and the temperature response. Another two tests are closer to the scenarios that arise from economic models. They test the climate module as a whole, that is, they incorporate gradual changes in CO2 emissions, exogenous forcing, and ultimately the temperature response. To illustrate the applicability of our method, we re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two other CMIP5 models that exhibit extreme but still permissible equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. By applying our comprehensive suite of tests, we show that i) both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and that ii) by re-calibrating their coefficients, we can match all CMIP5 targets we consider. Finally, we apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and the optimal warming. We find that in our updated model, the social cost of carbon is similar to DICE-2016. However, the optimal long-run temperature in our calibration lies almost one degree below that obtained by DICE-2016. Moreover, the social cost of carbon turns out to be much less sensitive to the discount rate than in DICE-2016. We explain how the model's climate part relates to these differences and also show that under the optimal mitigation scenario, the temperature predictions of DICE-2016 (in contrast to our proposed calibration) fall outside of the CMIP5 scenarios, suggesting that one might want to be skeptical about policy predictions derived from DICE-2016.
Keywords: climate change, social cost of carbon, carbon taxes, environmental policy, deep learning, integrated assessment models, DICE-2016
JEL Classification: C61, E27, Q5, Q51, Q54, Q58
Suggested Citation: Suggested Citation